Stimulated rock volume analysis

ABSTRACT

A data acquisition program, which includes core, image log, microseismic, DAS, DTS, and pressure data, is described. This program can be used in conjunction with a variety of techniques to accurately monitor and conduct well stimulation.

PRIOR RELATED APPLICATIONS

This application claims priority to U.S. Ser. No. 62/501,820 filed May5, 2017, and U.S. Ser. No. 62/519,450 filed Jun. 14, 2017, entitled“Stimulated Rock Volume Analysis.” Each of these applications isincorporated by reference for all purposes.

FEDERALLY SPONSORED RESEARCH STATEMENT

Not applicable.

FIELD OF THE DISCLOSURE

The disclosure relates generally to hydraulic fracturing. In particular,a data acquisition program using core, image log, microseismic,Distributed Temperature Sensing (DTS), Distributed Acoustic Sensing(DAS), and pressure data is used to monitor stimulation operations.

BACKGROUND OF THE DISCLOSURE

Unconventional reservoirs include reservoirs such as tight-gas sands,gas and oil shales, coalbed methane, heavy oil and tar sands, andgas-hydrate deposits. These reservoirs have little to no porosity, thusthe hydrocarbons may be trapped within fractures and pore spaces of theformation. Additionally, the hydrocarbons may be adsorbed onto organicmaterial of a e.g. shale formation. Therefore, such reservoirs requirespecial recovery operations outside the conventional operating practicesin order to mobilize and produce the oil.

The rapid development of extracting hydrocarbons from theseunconventional reservoirs can be tied to the combination of horizontaldrilling and induced fracturing (call “hydraulic fracturing” or simply“fracking”) of the formations. Horizontal drilling has allowed fordrilling along and within hydrocarbon reservoirs of a formation tobetter capture the hydrocarbons trapped within the reservoirs.Additionally, increasing the number of fractures in the formation and/orincreasing the size of existing fractures through fracking increaseshydrocarbon recovery.

In a typical hydraulic fracturing treatment, fracturing treatment fluidis pumped downhole into the formation at a pressure sufficiently highenough to cause new fractures or to enlarge existing fractures in thereservoir. Next, frack fluid plus a proppant, such as sand, is pumpeddownhole. The proppant material remains in the fracture after thetreatment is completed, where it serves to hold the fracture open,thereby enhancing the ability of fluids to migrate from the formation tothe well bore through the fracture. The spacing between fractures aswell as the ability to stimulate fractures naturally present in the rockmay be major factors in the success of horizontal completions inunconventional hydrocarbon reservoirs.

While there are a great many fracking techniques, one useful techniqueis “plug-and-perf” fracking. Plug-and-perf completions are extremelyflexible multistage well completion techniques for cased hole wells.Each stage can be perforated and treated optimally because the fractureplan options can be modified in each stage. The engineer can applyknowledge from each previous stage to optimize treatment of the currentfrack stage.

The process consists of pumping a plug-and-perforating gun to a givendepth. The plug is set, the zone perforated, and the tools removed fromthe well. A ball is pumped downhole to isolate the zones below the plugand the fracture stimulation treatment is then pumped in, althoughwashing, etching, and other treatments may occur first depending ondownhole conditions. The ball-activated plug diverts fracture fluidsthrough the perforations into the formation. After the fracture stage iscompleted, the next plug and set of perforations are initiated, and theprocess is repeated moving further up the well.

Improvements in recovery using fracking depend on fracture trajectories,net pressures, and spacing. Thus, the ability to monitor the geometry ofthe induced fractures to obtain optimal placement and stimulation isparamount. An induced fracture may be divided into three differentregions (hydraulic, propped, and effective), but out of the threefracture dimensions, only the last one is relevant to a reservoir model,and may be used to forecast future production.

Thus, what is needed in the art are improved methods of evaluating thehydraulic fracturing for every well being hydraulically stimulated.Although hydraulic fracturing is quite successful, even incrementalimprovements in technology can mean the difference between costeffective production and reserves that are uneconomical to produce.

SUMMARY OF THE DISCLOSURE

Described herein is a data acquisition program, which includes core,image log, microseismic, Distributed Temperature Sensing (DTS),Distributed Acoustic Sensing (DAS), and pressure data, for observing thereservoir state preceding and following hydraulic fracturing, andmethods of use in fracturing and producing hydrocarbon. Specifically,the hydraulic fracturing process can be characterized using a variety oftechniques to sample of the stimulated rock volume (SRV) at variouslocations to improve fracturing stimulations and improve hydrocarbonrecovery. The integration of the techniques allows for improvedcharacterization of the SRV and stimulated fractures, and ultimately, animprovement in the fracturing process. Further, the disclosed program isable to package the results for use with other commercial software.

The oil and gas industry came up with the concept of the stimulated rockvolume (SRV) as an empirical replacement for reliable modeling of highlycomplex fracture networks. The SRV represents the total volume ofreservoir rock that has been hydraulically fractured and itscalculations are based almost exclusively on the location ofmicroseismic events recorded during stimulation. However, methods toelucidate the extent of the stimulation of the reservoir rock providelittle details.

The most commonly used method to characterize the SRV is microseismicmeasurement, which locates and records microseismic events and is usedto map fracture density. However, microseismic measurements have a fewdisadvantages. First, it is an indirect method, as microseismicitycaptures the shear failure of well stimulation, but not tensile openingof the hydraulic fracture itself. In addition, the physical meaning ofmicroseismic events and how they relate to the hydraulic fracture isstill widely debated in the literature. Further, the method is subjectto a significant uncertainty in the location of the microseismic events.As such, by itself, this method does not accurately characterize ahydraulic fracturing procedure or SRV.

Thus, Applicants developed a method for combining microseismicmeasurements with other methods to accurately sample SRV andcharacterize fracking. Most of these methods are commonly employed inwells and thus do not add additional costs to the stimulationcharacterization. For instance, core, image logs, and pressure data arealso common logs performed during hydrocarbon recovery methods. Forfracking stimulations, these methods can aid in determining fractureorientation and width.

Other methods that are integrated by the program are not, historically,as commonly used, but are quickly becoming common place for other wellapplications as technology allows. These include Distributed AcousticSensing (DAS) and Distributed Temperature Sensing (DTS).

DAS is the measure of Rayleigh scatter distributed along the fiber opticcable. In use, a coherent laser pulse from an interrogator is sent alongthe optic fiber and scattering sites within the fiber itself causes thefiber to act as a distributed interferometer with a pre-set gaugelength. Thus, interactions between the light and material of the fibercan cause a small amount of light to backscatter and return to the inputend, where it is detected and analyzed. Acoustic waves, when interactingwith the materials that comprise the optical fiber, create small changesin the refractive index of the fiber optic cable. These changes affectthe backscatter characteristics, thus becoming detectable events. Usingtime-domain techniques, event location is precisely determined,providing fully distributed sensing with resolution of 1 meter or less.

Applicant has previous used DAS in a variety of fracturing monitoring inU.S. Ser. Nos. 15/453,650, 15/453,216, 15/453,584, 15/453,434,15/453,730, 15/453,044, all of which are incorporated herein for allpurposes.

Distributed Temperature Sensing (DTS) technology also includes anoptical fiber disposed in the wellbore (e.g. via a permanent fiber opticline cemented in the casing, a fiber optic line deployed using a coiledtubing, or a slickline unit). The optical fiber measures a temperaturedistribution along a length thereof based on an optical time-domain(e.g. optical time-domain reflectometry (OTDR), which is usedextensively in the telecommunication industry). One advantage of DTStechnology is the ability to acquire, in a short time interval, thetemperature distribution along the well without having to move thesensor as in traditional well logging, which can be time consuming. DTStechnology effectively provides a “snap shot” of the temperature profilein the well.

Thus, disclosed herein is a data acquisition program or method thatcollects and combines core, image log, microseismic, DAS, DTS, andpressure data into an easy to interpret format. Further, the data can beexported in a format usable by commercial oil and gas software. Theoutput of the disclosed program can also be combined with othertechniques to monitor all aspects of the fracturing process.

It should be noted that cross well seismic data is not included in theprogram because it is risky and produces inconclusive results. Further,gas is needed as a contrast agent in fractures and liquid filled proppedfractures are acoustically invisible.

Further, to collect the required cross well seismic data, each producerwell would need 2-3 monitoring wells for base state characterization(log, core data), pressure monitoring (pre- and post-fracturing) andmicroseismic monitoring. Also, 3-5 drill-through wells would be neededfor disturbed state characterization (log, partial core) and ‘smart’completions (local pressure monitoring/interference testing andinjection). By contrast, pressure gauges, DTS, traced proppants,production logs, and pressure transient data can all be acquired fromthe producer well.

In some embodiments, data from stimulations of multiple producer wellsare combined to properly characterize the stimulated fractures and theSRV.

The programs and methods described utilize non-transitorymachine-readable storage medium, which when executed by at least oneprocessor of a computer, performs the steps of the method(s) describedherein.

Due to the nature of the data pre- and post-transform, parallelcomputing and data storage infrastructure created for data intensiveprojects, like seismic data processing, are used because they can easilyhandle the complete dataset. Hardware for implementing the inventivemethods may preferably include massively parallel and distributed Linuxclusters, which utilize both CPU and GPU architectures. Alternatively,the hardware may use a LINUX OS, XML universal interface run withsupercomputing facilities provided by Linux Networx, including thenext-generation Clusterworx Advanced cluster management system. Anothersystem is the Microsoft Windows 7 Enterprise or Ultimate Edition(64-bit, SP1) with Dual quad-core or hex-core processor, 64 GB RAMmemory with Fast rotational speed hard disk (10,000-15,000 rpm) or solidstate drive (300 GB) with NVIDIA Quadro K5000 graphics card and multiplehigh resolution monitors. Alternatively, many-cores can be used in thecomputing. A Linux based multi-core cluster has been used to process thedata in the examples described herein.

The disclosed methods include any one or more of the below embodimentsin any combination(s) thereof:

In one embodiment, a method of optimizing the production scheme of ahydrocarbon-containing reservoir includes collecting image log,microseismic, Distributed Temperature Sensing (DTS), DistributedAcoustic Sensing (DAS), and pressure data from at least one observationwell in a hydrocarbon-containing reservoir to form a pre-stimulationdata set; while fracturing at least one well in a first fracturestimulation stage according to pre-determined fracturing parameters toform a set of fractures; collecting image log, microseismic, DistributedTemperature Sensing (DTS), Distributed Acoustic Sensing (DAS), andpressure data from at least said observation well in ahydrocarbon-containing reservoir to form a strain response data set;identifying said set of fractures formed in step b) from said strainresponse data set; characterizing the complexity of said set offractures; updating said pre-determined fracturing parameters based onsaid characterizing step; and, performing a second fracturingstimulation stage.

In another embodiment, a method of recovering hydrocarbons from ahydrocarbon-containing reservoir is described by drilling at least oneproducer well; drilling at least one observation well; installing aplurality of sensors for distributed acoustic sensing, microseismicmonitoring and a plurality of pressure gauges in each observation well;obtaining, microseismic, pressure, and DAS data from said observationwell to form a pre-stimulation data set; while fracturing at least oneproducer well in a first fracture stimulation stage according topre-determine fracturing parameters to form a set of fractures;obtaining, microseismic, pressure and DAS data from said observationwell to form a stimulation data set; identifying said set of fracturesformed in said fracturing step by comparing said pre-stimulation dataset and post-stimulation data; characterizing the complexity of said setof fractures; updating said pre-determined fracturing parameters basedon said characterizing step; and, performing a second fracturingstimulation stage; and, producing hydrocarbons.

In another embodiment, aA computer-implemented method for modeling thestimulated reservoir volume (SRV) of a hydrocarbon-containing reservoir,is described which includes drilling at least one producer well into anarea of said reservoir to be stimulated; drilling at least oneobservation monitoring well in said reservoir; installing a plurality ofsensors for microseismic monitoring and a plurality of pressure gaugesin each observation well; installing one or more fiber optic cables forDistributed Acoustic Sensing (DAS) in said observation wells, whereinsaid fiber optic cables are attached to interrogators obtaining, beforestimulation image log data, microseismic, pressure and DAS data fromsaid observation well to form a pre-stimulation data set; whilefracturing at least one producer well in a first fracture stimulationstage according to pre-determine fracturing parameters to form a set offractures; obtaining, during stimulation image log data, microseismic,pressure and DAS data from said observation well to form apre-stimulation data set; identifying said set of fractures formed insaid fracturing step by comparing said pre-stimulation data set andpost-stimulation data; characterizing the complexity, length, branching,density and other parameters of said set of fractures; and, modelingsaid SRV using said characterization of said set of fractures.

In another embodiment, a computer program product embodied on a computerreadable storage medium for characterizing a hydraulic fracturingstimulation, is described where integrated core data, image log data,microseismic data, Distributed Temperature Sensing (DTS) data,Distributed Acoustic Sensing (DAS) DATA, and pressure data from aprefractured zone of a reservoir, integrated core data, image log data,microseismic data, Distributed Temperature Sensing (DTS) data,Distributed Acoustic Sensing (DAS) DATA, and pressure data duringfracturing of a reservoir, computer code for estimating a stimulatedrock volume (SRV) that is being fractured from the data in a) and b).

These methods may be used to increase hydrocarbon production over wellswhere the method is not used. In some instances the image log and/ormicroseismic data samples a stimulated rock volume (SRV). Theobservation well may be a vertical well drilled near the production wellor the observation well may be one or more adjacent producer wells. Theobservation well may collect data from one or more adjacent producerwells. The methods described herein may be used for modeling thestimulated reservoir volume (SRV) of said reservoir. In someembodiments, the method may be repeated literately updating the model ofthe SRV.

-   -   Any method described herein, including the further step of        printing, displaying or saving the initial, intermediate or        final (or both) datasets of the method to a non-transitory        computer readable memory.    -   Any method described herein, further including the step of using        the final datasets in a reservoir modeling program to predict        reservoir performance characteristics, such as fracturing,        production rates, total production levels, rock failures,        faults, wellbore failure, and the like.    -   Any method described herein, further including the step of using        said final datasets to design, implement, or update a hydraulic        fracturing program in a similar reservoir, in a similar producer        well, or in subsequent fracturing stages of said reservoir.    -   Any method described herein, further including the step of        producing hydrocarbon by said reservoir.

This summary is provided to introduce a selection of concepts that arefurther described below in the detailed description. This summary is notintended to identify key or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in limiting the scope ofthe claimed subject matter.

“Fracking”, as used herein, may refer to any human process used toinitiate and propagate a fracture in a rock formation, but excludesnatural processes that fracture formation, such as natural seismicevents. The fracture may be an existing fracture in the formation, ormay be initiated using a variety of techniques known in the art.“Hydraulic Fracking” means that pressure was applied via a fluid.

As used herein, “fracture parameters” refers to characteristics offractures made using hydraulic fracking and includes fracture growth,fracture height, fracture geometry, isolation conditions between stages,stress shadows and relaxation, fracture spacing, perforation clusterspacing, number of perforation clusters/stage, well spacing, job size,pumping pressure, heel pressure, proppant concentration, fluid andproppant distribution between perforation clusters, pumping volume,pumping rate and the like.

As used herein, a “fracture model” refers to a software program thatinputs well, rock and fracturing parameters and simulates fracturingresults in a model reservoir. Several such packages are available in theart, including SCHLUMBERGERS® PETREL® E&P, FRACCADE® or MANGROVE®software, STIMPLAN™, tNAVIGATOR™, SEEMYFRAC™, TERRAFRAC™, ENERFRAC®,PROP®, FRACPRO™, ROCKFIELD ELFEN™, ALTAIR GEOD™, Barree & AssociatesGOHFER®, and the like. For shale reservoirs, FRACMAN™ and MSHALE™ may bepreferred. These models can be used with appropriate plugins ormodifications needed to practice the claimed methods.

By “fracture pattern”, we refer to the order in which the frack zonesare fractured.

The term “fracture complexity” refers to the degree of entanglement (orlack thereof) in the induced fractures. Fractures can range from simpleplanar fractures to complex planar fractures and network fracturebehavior. Further, the fracture complexity can change from near-well,mid-field, and far-field regions.

As used herein, the “Gaussian Kernel” or “radial basis function kernel”aka “RBF kernel” is a popular kernel function used in various kernelizedlearning algorithms. In particular, it is commonly used in supportvector machine classification.

As used herein, a “drill through well” a well whose trajectory isdeliberately planned to sample some portion of the SRV.

As used herein, a “monitoring” well is a well nearby a producer that isused to monitor a producer. It produces samples and data for controlpurposes.

As used herein, “single well seismic imaging” or “SWSI” is theapplication of borehole seismic sources and receivers on the same stringwithin a single borehole in order to acquire CMP type shot gathers.“Cross well” seismic places sources and receivers in adjacent wells inorder to image the interwell volume.

The term “many-core” as used herein denotes a computer architecturaldesign whose cores include CPUs and GPUs. Generally, the term “cores”has been applied to measure how many CPUs are on a giving computer chip.However, graphic cores are now being used to offset the work of CPUs.Essentially, many-core processors use both computer and graphicprocessing units as cores.

The use of the word “a” or “an” when used in conjunction with the term“comprising” in the claims or the specification means one or more thanone, unless the context dictates otherwise.

The term “about” means the stated value plus or minus the margin oferror of measurement or plus or minus 10% if no method of measurement isindicated.

The use of the term “or” in the claims is used to mean “and/or” unlessexplicitly indicated to refer to alternatives only or if thealternatives are mutually exclusive.

The terms “comprise”, “have”, “include” and “contain” (and theirvariants) are open-ended linking verbs and allow the addition of otherelements when used in a claim.

The phrase “consisting of” is closed, and excludes all additionalelements.

The phrase “consisting essentially of” excludes additional materialelements, but allows the inclusions of non-material elements that do notsubstantially change the nature of the invention.

The following abbreviations are used herein:

ABBREVIATION TERM SRV simulated rock volume DTS Distributed TemperatureSensing DAS Distributed Acoustic Sensing G gauges bbl oil barrel Pproducer well S data well ST Sidetrack FMI-HD Fullbore FormationMicroimager-High Definition CT Computer tomography TVD True verticaldepth NE Northeast SW Southwest DFIT Diagnostic Fracture InjectionTesting SEM Scanning electron micrograph

BRIEF DESCRIPTION OF DRAWINGS

The application file contains at least one drawing executed in color.Copies of this patent application publication with color drawing(s) willbe provided by the Office upon request and payment of the necessary fee.

FIG. 1. Pilot well lay-out, map view.

FIG. 2. Well paths showing hydraulic fractures as white discs. Coredintervals are shown in pink. Pressure/temperature gauge location in S3shown by red circles Yellow filled log on P3 is Scandium RA tracer log.Blue discs show locations of iridium RA tracer from offset producer P2.

FIG. 3. Map view and cross-section view of microseismic events.

FIG. 4. Microseismic and pressure at S1.

FIG. 5. Strain rate in S3 from DAS during stimulation of offsetproducer. The red signal denotes fiber extension and blue denotes fibercompression.

FIG. 6. Cross well DAS response indicating that some fractures extend1,500 ft.

FIG. 7A-C. DAS injection monitoring, wherein FIG. 7A shows the injectedslurry volume for each stage; FIG. 7B is an example of the DAS data fromstage 5; and FIG. 7C is a summary of hydraulic stimulation injectiondata.

FIG. 8. DTS injection monitoring.

FIG. 9A-C. Hydraulic fractures in close association. FIG. 9A showingdipping fractures in core. FIG. 9B showing the same section of CT imageas an unwrapped circumferential image. FIG. 9C showing 18 ft section ofFMI-HD™ image log containing several fracture doublets and tripletsshowing up as dark sinusoids across image.

FIG. 10A-D. Hydraulic fracture swarm. FIG. 10A is a core photograph;FIG. 10B is the unwrapped circumferential CT scan of cored section; FIG.10C is the FMI-HD™ image of cored section; and, FIG. 10D shows onehydraulic fracture swarm consisting of 22 fractures in a 20 ft sectionof the well.

FIG. 11. Hydraulic fracture distribution where ‘P3’ marks the depth thatthe well crosses the P3 producer.

FIG. 12. Fracture distribution along the well paths. Green curve isGaussian Kernel of microseimic density. Blue curve is Gaussian Kernal ofhydraulic fracture density and actual fracture location along eachwellbore is shown by blue vertical lines at the base of each plot.

FIG. 13. Modeling warmback at an injection well. 5 fractures (green), 12fractures (purple), 25 fractures (red) and 25 irregular fractures (blue)were modeled for 3 day warmback in an injection well.

FIG. 14. Distribution (Volume)≠Frac Spatial Density

FIG. 15. Real DTS data

FIG. 16. Residual frac cooling effect is long lasting

FIG. 17. Thermal Radius of Investigation.

FIG. 18. Cross-flow

DETAILED DESCRIPTION

The invention provides a novel data acquisition program or method formonitoring hydraulic fracturing and sampling stimulation rock volume(SRV). Specifically, the data acquisition program integrates core, imagelog, microseismic, DTS/DAS, and pressure data to monitor a reservoirbefore and after fracturing. In more detail, the data acquisitionprogram characterizes the hydraulic fracture and SVR by:

-   -   Sampling the SRV via a drill-through well (logs and core).        Spatially limited but detailed, high fidelity fracture and SRV        data.    -   Active and passive seismic imaging.    -   Microseismic event location and size.    -   “Low” resolution images of larger reservoir volume before and        after fracturing.    -   Calibrate to detailed drill through well data.    -   Pressure monitoring, tracing and pressure build-up/interference.    -   Identify hydraulic connections.    -   Estimate fracture conductivity.    -   Far field pressure response during production and interference        testing.    -   Model inferred fracture description that matches production and        pressure behavior.

The integrated characterization by the data acquisition program can thenbe used to update fracturing simulations or parameters to improve oilrecovery and can be combined with other techniques to accurately monitorand conduct well stimulation.

The present invention is exemplified with respect to the Shale 1 Pilotdescribed below; however, this is exemplary only, and the invention canbe broadly applied to an unconventional reservoir that requireshydraulic fracturing stimulations. The following examples are intendedto be illustrative only, and not unduly limit the scope of the appendedclaims.

Shale 1 Pilot

The described data acquisition program was applied to a pilot area on ashale reservoir, hereinafter referred to as Shale 1, in Texas. The Shale1 reservoir was chosen because it was an active play that was wellcharacterized and had established field procedures.

The design of the pilot area is shown in FIG. 1. The pilot areaconsisted of 4 producers, P2, P3, P4 and P5 landed at the same level inthe Cretaceous Lower Shale 1, one vertical far field pressure monitoringwell, and 5 deviated observation wells to characterize the stimulatedrock volume at different locations adjacent to one of the stimulatedproducers, the P3 well. The wells were drilled in 2014 and 2015 adjacentto an existing producer, P1.

The pilot area was structurally quiet with beds dipping gently to thesoutheast at 3° without seismically mappable faulting. Shale 1 isoverlain by the Austin Chalk and underlain by the Buda Limestone. Thelower portion of Shale 1 in the pilot area consists primarily of thinlyinterbedded organic marl, marly limestone and limestone beds. The upperportion of Shale 1 above the pilot area is a calcareous mudstone.

The 4 producers were drilled from a single pad, down dip, parallel tobedding in a fan-shaped arrangement with well-spacing beingapproximately 400 ft. at the heel and 1,200 ft. at the toe (FIG. 1). Allwere landed at the same stratigraphic depth, approximately 70 ft. abovethe Buda Limestone. The P3 well was instrumented with fiber optic cablesfor bottom hole pressure and Distributed Temperature Sensing (DTS) andDistributed Acoustic Sensing (DAS) for monitoring during completion andproduction. The P2, P4 and P5 were not instrumented down hole, but weremonitored at the surface.

Three data wells were drilled next to P3. S1 is a vertical well drilledapproximately 615 ft. to the southwest of P3. A standard log suite wasacquired to establish stratigraphy for geosteering. Additionally, theBorehole Acoustic Reflection Survey (BARS™) and Next Generation Imager(NGI™) logs were obtained for fracture characterization. S1 was designedfor simultaneous pressure and microseismic monitoring. Fiber optic cablewas installed on casing for monitoring DTS/DAS and reservoir pressurethroughout the Shale 1 interval and into the Austin Chalk. During thestimulation of P2 and P3, geophones were placed in this well as part ofa dual well microseismic acquisition.

The S2 and S3 wells were landed about half way along P3 to sample theSRV in the central-to-toe region, adjacent to stages 1-7. S2 was drilledbefore the hydraulic stimulation of the producers to characterize thenative state of fracturing in the pilot area. It was 30 ft. TVD aboveand approximately 200 ft. southwest of the P3. Lateral length is 1,270ft. Two hundred feet of three-inch diameter horizontal core and anFMI-HD™ log were taken in this well. S3, which was sidetracked threetimes, was used to sample the SRV at different spatial locations aroundP3 post-stimulation.

Cuttings were collected and examined for the presence of proppant in allpost-stimulation wells. The sidetracked laterals were 1,300 to 1,700 ft.long. Pipe conveyed FMI-HD™ image logs were run in all sampling wellsfor fracture characterization. The original S3 wellbore was parallel to,30 ft. TVD above, and 70 ft. northeast of the P3, and 360 ft. ofthree-inch diameter, horizontal core was taken from this well.

After logging, the lateral was cemented and abandoned using a disposabletubing string. This drilling and abandonment procedure was repeated forS3_STO1 and S3_STO2.

STO1 was drilled at the same stratigraphic level as the original S3lateral. The sidetrack initiated approximately 130 ft. northeast of theproducer and sampled outward to 360 ft. from P3. The second and thirdsidetracks landed approximately 210 ft. to the northeast and 100 ft.above P3 and both crossed above it.

STO2, remained 100 ft. above the producer along its entire length with aTD approximately 105 ft. southwest of the producer, whereas STO3 cutdown through the section, crossed 56 ft. above the producer to a TDapproximately 30 ft. below and 250 ft. to the southwest of P3. 120 ft.of three-inch diameter core were taken from STO3, roughly 40 ft. abovethe core taken in the original S3 lateral.

The S3_STO3 well was cased and cemented to serve as a long-termfar-field pressure monitoring well. Twelve externally mounted pressuregauges were installed along the length of the lateral. Distances fromgauge to the P3 producer ranged from 50 to 280 ft. In S1, the 7pressure/temperature gauges were installed from just above the BudaFormation up into the lower Austin Chalk using the cement annulus asisolation. Each gauge was in a casing mounted carrier connected by asnorkel tube to an externally mounted perforating gun assembly. Afterdeployment, the guns were fired outward to connect the pressure gaugesto the formation. Unfortunately, while all seven gauges remainedfunctional, only three were successfully hydraulically connected to theformation. FIG. 2 displays the well paths showing the hydraulicfractures as discs and locations of the gauges.

Pilot field operations took place in two phases over two years. In thefirst phase, the producers and sample wells S1 and S2 were drilled andcompleted. The producers were then stimulated and put on production forone month before sample well S3 was drilled. Phase 2 consisted of the 3sidetracks from S3 and was performed after a year of production.

Completion Monitoring

The disclosed data acquisition program for Shale 1 pilot relied heavilyon spatial sampling adjacent to a horizontal producer, both before andafter hydraulic stimulation, to characterize the state of hydraulicfracturing. Remote monitoring by microseismicity and DistributedAcoustic and Temperature Sensing (DAS/DTS) were an integral part of theprogram design. Furthermore, the design employed multiple pressuregauges to monitor the spatial progress of depletion with the intent totie production performance to observed fracture characteristics.

Thus, this multiwell stimulation was monitored by various meansincluding: dual well microseismic; continuous distributed acousticsensing (DAS) and distributed temperature sensing (DTS) in P3; andpressure response in multiple gauges in S1 and P3. The variousmonitoring means were integrated into an easy to view format allowingfor quick decisions on the fracturing program parameters.

In more detail, borehole microseismic data was recorded duringstimulation of the P2 and P3 using high temperature borehole geophonesclamped to the inside of casing in vertical monitor well S1 and throughthe build section of a horizontal monitor well S2. Both arrays consistedof twelve geophones spaced at 100 ft intervals. In the vertical monitorwell, the bottom geophone was placed 100 feet above the top of the Budaand the array extended vertically 1100 ft. All geophones were at orabove the level of the producers. Downhole conditions of 325° F.exceeded the rated maximum temperature/pressure conditions for thegeophone arrays and thus, geophones were frequently replaced.

In all, 26 of the 28 stages in P2 and P3 were microseismically monitoredfrom at least one of the monitoring wells and the events from six stagesin P3 closest to the sample wells were recorded on both arrays. Industrystandard event detection and location routines were used to obtainrobust dual well location solutions for the 6 stages offsetting thesample wells, and a combination of single and dual well solutions wereobtained for the remaining stages. These differences in microseismicacquisition and processing, led to variation in the completeness ofevent detection and the accuracy of event location over the monitoredarea; however, the greatest confidence in event locations were assignedto the immediate pilot study area.

Vertically, half of the microseismic events are contained within aninterval from 15 ft below to 115 ft above the P2 and P3 wells. A lack ofevents in the Buda limestone suggested that formation behaved as abarrier to downward fracture propagation. The density of microseismicevents were greatest at the wellbore and decrease spatially away fromthe stimulated well (FIG. 3). Stage event patterns ranged from linear todispersed.

The central stages of the laterals show NE/SW trending linear eventclustering features which extend over 1,000 ft from the stimulated wellsand cross P1, P2 and P3. This clustering was perpendicular to theminimum horizontal in-situ stress and was thus parallel to the predictedplane of hydraulic fracturing. The linear event clustering features area result of events recorded during multiple stages and sometimesconsisted of co-located events from both the P2 and P3 stimulation.

When the microseismic events were examined stage-by-stage, it wasapparent that, in some stages, events occur both uphole and downhole ofthe stage being stimulated. A significant amount of microseismicactivity recorded during the stimulation of the P2 was located along theP1 well, which had been stimulated and produced for one year prior tothe pilot activities.

Few microseismic events were recorded in the heel and toe areas of P2and P3. The lack of events in the heel region was likely the result ofpoor geophone location for imaging this area. Considering observationsfrom previous microseismic surveys that consistently show a high levelof activity in the near wellbore region, the paucity of events in thetoe region of P3 was puzzling.

Microseismic events also extended to the S1 well, which had a verticalpressure gauge array and was 615 feet from P3. As FIG. 4 indicates, therecording of microseismic events positioned within 100 feet of S1preceded and persisted throughout the pressure response registered inthe three gauges successfully connected to the reservoir (G3, G5 andG6). The absolute maximum-recorded pressure in the connected gaugesexceeded the minimum stress of 11,960 psi estimated by DFIT from anadjacent well. Hence, it is interpreted that a hydraulic fracture orfractures intersected or were proximal to S1. Significantly, thepressure event was associated with a compressional heating eventrecorded in all 7 gauges. Given that the magnitude of heating wassimilar in gauges 1 through 6, it is likely that the fracture extendedthroughout the Shale 1 interval.

FIG. 4 also shows the DAS response at the S1. The DAS signal is highlysensitive to temperature and mechanical strain changes. In thisinstance, the fiber was used to analyze the strain response at S1imparted by stimulating the P3. The first DAS arrival was consistentwith the first recorded local microseisms in time and depth. SubsequentDAS arrivals agreed spatially and temporally with recorded pressure andtemperature events, which were interpreted above as fracture arrivals.Given that it provides precise position data, the spatial resolution offracture height was extended to the base of the Austin Chalk at the S1location.

The DAS fiber in P3 was also used to analyze strain changes during thestimulation of the adjacent producers. The fiber is mechanically coupledwith the formation, thus strain rate along the P3 wellbore duringhydraulic fracturing of the offset producers can be calculated and tiedback to formation deformation. Where the fiber is in the path of ahydraulic fracture, it was extended.

On either side of the hydraulic fracture, the fiber and coupledformation were compressed, or stress shadowed. This is illustrated inFIG. 5, which shows an example of a recorded signal at P3 duringstimulation of an offset producer. In this figure, the red signaldenotes fiber extension and blue denotes fiber compression.

The response in the offset well correlated with the fluid and proppantinjection timing. A set of extension signals interpreted to be ‘newfracture opening’ (in red) were observed within a short delay of onsetof pumping, which is surrounded by the compression signal from thestress shadowing of the formation. The signal was reversed when pumpingstops resulting in ‘fractures closing’ (in blue) surrounded by arelaxation of stress in surrounding formation. The location and numberof fracture hits observed can be correlated back to the perf clustersfor each stage on the stimulation well to provide information on SRVgeometry (FIG. 6). Note that some of the fractures extend for 1,500 ft.There is an absence of signal from the toe stages of P5 because thesestages were not monitored.

The DTS/DAS interpretation of the injected fluid distributions at thecluster level for well P3 is shown in FIG. 7A-C and FIG. 8. All clustersin each stage produced a measurable amount of acoustic energy throughoutthe time of pumping. This was the first qualitative indication that allclusters initiated fracturing and took a meaningful amount of fluidvolume during the stimulation. FIG. 7B, in which acoustic energy isplotted against time, shows an example of the DAS data from stage 5. Thered/yellow colors in the figure represents the high acoustic intensityrecorded from the DAS at each of the 5 cluster locations. The acousticenergy varied somewhat with time, but was continuous throughout thepumping of the stage.

DTS interpretation in FIG. 8 showed cooling across all clusters, whichsupports the DAS conclusion that all clusters took fracture fluid. Inthis figure, where temperature is plotted against time, the red colorindicates higher temperatures and blue lower temperatures. Each stagecan be identified in depth by the horizontal dashed lines representingthe plug depths. The lowest temperatures recorded were at the depths ofthe perforation clusters associated with the stage being pumped. In FIG.8, multiple stages show cooling below the plug that was intended toprovide hydraulic isolation from the previously pumped stage. In 10 ofthe 13 stages monitored, fluids were leaking below the plug and stageisolation was not complete.

The DAS data was also used in a quantitative sense to interpret plugleakage and injected fracture fluid (slurry) volumes by cluster. In thisproprietary method, it was assumed that the DAS intensity as a measureof the flow volume through each perforation. The results from stages 2-8are shown in FIG. 7A. While it verified that all clusters tookmeaningful amounts of fracture fluid, the quantitative analysis showedthat the distribution of slurry volume into each cluster is uneven withclusters taking from 33 to 142% of the targeted cluster volume. Thisanalysis also indicated that, for the entire wellbore, fluid lossthrough the plug ranges from zero in three stages, to small in fourstages, to approximately 500 bbl or 10% of the total pumped volume insix of the stages.

The disclosed data acquisition program utilized the core, image log,microseismic, DTS/DAS, and pressure data gathered from the pilot beforeand after the hydraulic fracturing.

Core and Image Logs

In this study, it was necessary to distinguish between hydraulic,natural and drilling induced fractures. In most cores, identification ofnatural and drilling induced fractures is relatively routine usingfracture mineralization, surface markings, and orientation and form withrespect to the core axis (Kulander et al. 1990). However, in the absenceof natural fracture mineralization or a distinct difference inorientation, hydraulic and natural fractures can be hard to distinguish.To characterize the natural fracturing in this area, which was unknown,a pre-stimulation, baseline core and image log were acquired. Well S2,was drilled at the same stratigraphic depth and just 270 ft. alongstrike from S3, the first post-stimulation sample well.

When interpreting the hydraulic fracture pattern within the SRV, it mustbe noted that, except for the toe-ward end of ST03, all the wells cutthe SRV above the level of the stimulated producer, and all cores wereacquired above the level of the stimulated wells. Despite sampling over7,700 continuous feet of the SRV volume, the overall geometry of the SRVremains statistically under-sampled with key areas immediately adjacentto and below the producer being completely unsampled.

The pre-stimulation S2 core was taken with a mud system that wassignificantly over balanced. This resulted in the formation of manydrilling induced fractures in the core and borehole wall perpendicularto the wellbore. In the core, these were identified primarily by thepresence of distinct surface arrest lines that initiated a fewmillimeters from the edge of the core and typically wrapped around theupper, but occasionally also the lower, half of the core. These drillinginduced fractures were present in every foot of the S2 core. They werealso abundant in the image log from the well.

The 200 ft of core from S2 contained just 4 natural fractures. Thenatural fractures are not mineralized and trend NE/SW with 75-80° dipsto the SE. The image log from the vertical S1 well contained a singlenatural fracture within Shale 1 and it parallels the S2 fractures. Thispaucity of fracturing supported the belief that the pilot was placed inan area without faulting and with very limited natural fracturing. Thesenatural fractures differ in orientation from the drilling inducedfractures by just 10-15°. This similarity in orientation causedchallenges to fracture classification from the image logs alone and wasa factor in angling the later Phase 2 sidetrack wells away from theprincipal stress direction to create a larger angular difference betweenthe fracture types and facilitate their identification in the imagelogs.

Although the 4 natural fractures from the S2 core were interpreted to beun-mineralized, a hydraulic origin could not be absolutely eliminated.Well P1, the original well on this lease, which is approximately 1,300ft to the northeast and well within the DAS recorded envelope for crosswell events, was completed a year prior to pilot operations. Therefore,the fractures could also be hydraulic in origin from well P1.

The cores from S3 and S3 (STO3) contained many hydraulic fractures andfar fewer drilling induced fractures. Both wells were drilled with mudmuch closer to the formation pressure and no stress indicators such asbreakouts or drilling-induced tensile fractures were observed. Thehydraulic fractures have the following characteristics: un-mineralized;oriented NE/SW transverse to the well; steeply dipping; planar with,when present, surface markings indicative of extensional or hybridorigin; and non-uniformly spaced. The case for a hydraulic origin isdeductive.

First, these fractures were not present in the baseline core just 270 ftaway and in this structurally quiet area it is unlikely that hundreds ofnatural fractures would form along trend over such a short distance.Second, they are aligned with both present day stress and the linearevent clusters seen in the microseismic; thus, they parallel theanticipated hydraulic fracture direction. Third, surface features, suchas arrest lines and plumose features (having many fine filaments orbranches that give a feathery appearance), indicate an extensional orhybrid (mixed-mode) origin. Finally, embedded proppant was found on thesurface of two of the hydraulic fractures. Although a reactivatednatural fracture origin cannot be eliminated, it is much less likely.

While the hydraulic fractures are generally planar, their surfaces maybe smooth or occasionally stepped. Fracture surface roughness isaffected by lithology. The fractures within the organic marl beds aregenerally extremely smooth whereas those in the more calcareous layersdisplay small ridges parallel to bedding and may have arrest lines orplumose features indicative of upward and lateral fracture propagation.Surface features indicating shear are absent. The cores contain examplesof the hydraulic fractures being refracted at bedding surfaces and ofbent arrest lines and one hydraulic fracture has a 3-mm step where itcrosses a bedding surface. Ridges and steps in the hydraulic fracturesurfaces have implication for proppant transport and settling andfracture permeability preservation during pressure draw down.

Both in core and image logs, it was observed that multiple (2-3)hydraulic fractures often develop in close association, where theirorientations differ by 5-20° and they diverge with a projected line ofintersection, or branch line, just outside the core or borehole wall(FIG. 9A-B). The common occurrence of these doublets and triplets alongthe length of the wells indicates that hydraulic fracture branching maybe widespread. Branching along with the observed influence of beddingsurfaces on hydraulic fracture propagation leads to the postulation thatthe mechanical stratigraphy resulting from interbedded organic marl andstiffer limey beds is in part responsible for much of the observedfracture complexity and the large number of fractures encountered. Othernatural heterogeneities in the formation likely also impact fracturecomplexity.

The core adjacent to the hydraulic fractures was intact, with no visibleor microscopic evidence of off-fracture damage that might enhance matrixpermeability. This was supported by SEM mapping and steady state coreplug permeability measurements from both the pre- and post-stimulationcores that were acquired from the same lithologic interval. In thepost-stimulation case, plugs were acquired proximal and distal tohydraulic fracture faces. Regardless of origin, these samples showed nostatistical difference in microscopic structure or measuredpermeability.

The 3 cores were also oriented using the bed orientation from the imagelogs, apparent bedding orientation in the core, and the boreholeorientation. Core and image log fracture orientations were the same. Thehydraulic fractures formed a parallel set striking N060° E and dipping75-80° SE. The strike and dip both have a ±20° range, some of which canbe ascribed to the accuracy of the core orientation method, but much ofwhich is real and can be seen in continuous sections of core. Thishydraulic fracture strike was anticipated and was consistent with thelocal in-situ stress field. The 75-80° dip of the fractures indicatesthat either these fractures are not pure opening mode but hybrid mode1-2, or the in-situ principal stresses are rotated away fromvertical/horizontal. A small sub-set of the fractures, especially in theshallower wells ST 02 and ST 03 dip to the northwest. It is unclearwhether these fractures are more highly influenced by branching orsplaying mechanisms, influence from local mechanical heterogeneities, oroperational stress perturbations.

The distribution of hydraulic fractures along the wellbores wasnon-uniform. In both post stimulation cores, the hydraulic fracturesform swarms (clusters), in which many fractures are spaced a few inchesapart and are separated by lengths of core with several feet betweenfractures. The FMI-HD™ image log data, which were of good to excellentquality, were also used to analyze the spatial distribution of hydraulicfractures within the SRV. The best quality FMI-HD™ logs were taken in S3where, over the cored section, each hydraulic fracture in the core couldbe correlated to a fracture in the image log. The image log, however,did not resolve closely spaced fractures and showed some stretch andcompression compared to the core. Nevertheless, a high degree ofconfidence was established in the image log interpretation such thatdipping hydraulic fractures could be distinguished from drilling inducedfractures, which were perpendicular to the well trajectory.Interpretation of the image logs from the sidetracks, especially theSTO3, was more challenging due to higher borehole rugosity andtortuosity that resulted in tool sticking and short sections withoutinterpretable images.

S3 and its sidetracks were sampled adjacent to stages 1-7 in the P3producer. Thus, they sampled the rock volume stimulated by 30perforation clusters. The number of hydraulic fractures interpreted inthe image logs is shown in Table 1 and far exceeds one per perforationcluster.

TABLE 1 Number of Hydraulic Fractures from Image Log Interpretation WellLength of Image Log (ft.) # of Hydraulic Fractures S3 1,378 680 S3 ST011,748 423 S3 ST02 1,583 397 S3 ST03 1,735 966

Hydraulic Fracture Density

To investigate the spatial characteristics of the SRV, the hydraulicfracture density is presented in simple histograms (FIG. 11) where thefracture count in a 50 ft. window is displayed. Even at a bin size of 50ft. the hydraulic fracture intensity is non-uniform. These plots showthat overall hydraulic fracture intensities are highest in S3 and STO3and lower in STO1 and ST02, which are drilled further out laterally andhigher above the producer respectively. Fracture intensity decreases inSTO1 at a measured depth of 14,750 ft., which is 40 ft above and 270 ftlaterally offset from the stimulated well. While the upper few hundredfeet of STO2 shows fracture densities similar to S3 and ST03, thetoe-ward two-thirds of the well has much lower intensities even wherethe well crosses 100 ft directly above the producer, as shown in FIG.11. Thus, while some hydraulic fractures extend well beyond the sampledarea, hydraulic fracture intensities decrease more rapidly with heightthan with lateral distance. This implies that the SRV is considerablywider than it is tall.

Proppant

The RA tracer log from P3 indicates that proppant was well distributedamongst clusters at the source location (FIG. 2). To determine proppantdistribution in the sample wells, a two-part proppant study wasperformed. First, cuttings were collected at a 20 ft. intervalthroughout the drilling and coring of the sample wells. The samples werewashed, dried and sieved through a 70-mesh screen to remove fineparticles and then were examined visually and the abundance of proppantgrains was estimated semi-quantitatively. The samples containingproppant for all 4 post-stimulation wells are tabulated in Table 2. Theshallowest proppant grain was encountered in S3_STO2 approximately 120ft. TVD above the producer. Proppant was much more abundant in S3, whichis the lateral that is consistently closest to the producer, whereproppant was detected in 76% of the cuttings samples. Conversely, just5% of the cuttings samples in STO1, which is the shallowest well at 100ft. above the producer, contained proppant.

TABLE 2 Proppant Grain Distribution in Cuttings Samples Well # Samples %Containing > 1 Proppant Grain S3 89 76% S3 ST01 143 21% S3 ST02 146  5%S3 ST03 103 15%

The second part of the proppant work involved visual inspection of thesurface of each cored fracture for sand gains and proppant indentations.All mud and debris from the fracture surfaces were collected forlaboratory analysis. Small numbers of proppant grains were found on manyhydraulic fracture surfaces. In S3, at least one grain of proppant wasrecovered from 25% of the fracture surfaces, whereas in the STO3 corejust 3 fractures contained proppant (5%). It is unknown whether thesesand grains were in-situ or had been washed into the hydraulic fracturesalong with drilling mud.

Only two cored hydraulic fractures had sand grains embedded on theirsurfaces, one was in S3 and the other in STO3. Embedment pits in thesurfaces of these two fractures, along with the presence of many sandgrains, indicated that the proppant was in-situ and had not been washedin with the mud system. An estimate of the thickness of the proppantpack was not possible given the mud invasion. The presence of proppantin the cuttings and core confirms that the wells sampled some portion ofthe propped SRV and the proppant is more abundant at the S3 locationthan in wells drilled further from the producer.

Fracture Characteristics

The observations from this pilot lead to a new and differentunderstanding of the SRV. It was concluded that reservoir permeabilityenhancement in the SRV results principally from hydraulic fractures andthat matrix damage is extremely limited or absent. Hydraulic fractureswere numerous, widespread, closely spaced, steeply dipping, and branch.Most form a near parallel set. Hydraulic fracture surfaces were roughand may step where they cross bedding planes. Proppant emplacement, atthe sampled locations, was sparse. Some hydraulic fractures are verylong and extend well beyond the sampled area. The limited spatial dataindicates that hydraulic fracture intensity decreases more rapidly withheight than with lateral distance and that the SRV volume in this areacould be on the order of two to three times as broad, laterally, as itis tall. This shape was generally consistent with the shape of themicroseismic event cloud.

The broadly parallel nature of hydraulic fractures and their largenumber indicated that SRV permeability is likely to be highlyanisotropic on a reservoir scale. The rugosity of the hydraulic fracturesurfaces will influence both proppant transport and settling. Thesparsity of proppant, especially at more distant locations in the SRV,indicated that fracture permeability and its preservation duringpressure draw down may be spatially heterogeneous.

These findings are very different from the simple view of the SRV thatare commonly modeled or predicted with current fracture models. Theabsence of proppant on most of the hydraulic fractures indicates thatproppant emplacement is quite different from idealized transport modelpredictions. The apparent side-by-side propagation of closely spaced,near parallel hydraulic fractures also differs from the output ofcurrently accepted fracture models and may call into question the roleof stress shadowing in hydraulic fracture propagation. Stress shadowingmay have contributed to non-uniformity, but did not cause fractures toturn severely or fully inhibit the propagation of closely spacedfractures.

Correlations

The relationship between fracture density and cluster spacing wasinvestigated by calculating a Gaussian Kernel Function, with a bandwidthof 6 feet, from the S3 well and is shown in FIG. 12. Fourier spectralanalysis was applied to determine the periodicity of densely spacedfractures or swarms. Data adjacent to stages 1 through 7 of well P3 wereanalyzed. Stages 3, 4, and 5 exhibit a signal with a swarm spacing of 45feet. The average cluster spacing was 47 feet. Stage 1 was not expectedto show a strong dependence because this stage was not completed. Hence,3 of 6 completed stages reflect a positive correlation of swarmoccurrence with treated cluster spacing projected from the adjacentstimulated well, whereas 3 do not.

Understanding the relationship between the observed hydraulic fracturesand microseismic events was complicated by the different scale at whichthe two measurements are recorded and the discrete nature of bothevents. A probability density function of the discrete location of bothwas calculated using the approach of Silverman (1986), which results ina smooth distribution using a Gaussian Kernel. To determine thebandwidth for construction of the density estimates, the method ofSheather and Jones (1991) was adopted. The relationship between the twomeasurements was determined by cross plotting and computation of aPearson correlation.

The correlation of microseismic events to sampled hydraulic fracturedensity are summarized in Table 3 and FIG. 12. Although multiplecombinations of microseismic attributes and fracture characteristicswere examined, only a few showed any degree of correlation. The totalhydraulic fracture population associated with wellbores S3, S3_STO1 andS3_STO3 showed none to moderate positive correlation to microseismicevent density; notably, these correlations improved when the hydraulicfracture population was restricted to include only shallowly dippingfractures (<70°). However, similar dependencies were not exhibited insidetrack S3_STO2.

Given that over 75% of the total hydraulic fracture population lacks astrong positive correlation to microseismic event density, it isdifficult to conclude that event maps can be used as a proxy forfracture density or for that matter, effective permeability. In somecases, assuming such a relationship could be misleading. For example,hundreds of fractures were sampled in the toe region of the P3 well andyet microseismic events at this location are quite scarce (FIG. 3). Thisobservation is not meant to imply that microseismic data is not usefulin delineating the character and morphology of fracturing; it is simplyinsufficient to explicitly define the outcome in a quantitativelyreliable manner.

TABLE 3 Correlation Between Gaussian Kernel Density of HydraulicFractures and Microseismic Maximum S3 S3 ST01 S3 ST02 S3 ST03 S3 # S3 ST01 # S3 ST 02 # S3 ST03 # Dip Correlation Correlation CorrelationCorrelation Fractures Fractures Fractures Fractures 90 −0.07 0.58 −0.100.11 680 423 397 966 85 −0.11 0.52 −0.12 0.15 592 351 382 821 80 0.060.54 −0.12 0.21 414 230 342 578 75 0.64 0.69 −0.14 0.35 163 103 287 34770 0.78 0.75 −0.15 0.56 70 47 238 195 65 0.8 0.79 −0.23 0.58 41 28 282111 60 0.79 0.73 −0.34 0.73 29 15 121 53 55 0.71 0.78 −0.44 0.76 11 1079 29

A similar correlation technique was employed to determine therelationship between fracture density and total fluid injected at thecluster level as calculated from DAS data. A Pearson CorrelationCoefficient of 0.13 was obtained, indicating essentially no correlationbetween the two data sets.

Model Results

FIG. 13 shows a fracturing model for warmback at an injection well.Slurry volume through each cluster was measured with DAS & perforationcamera data as shown in FIG. 14. Both data sets confirmed uneven butpositive throughput of slurry through every perforation. In FIG. 15,slurry volume through each cluster was measured with DAS & perforationcamera data. Both data sets confirmed uneven but positive throughput ofslurry through every perforation.

Residual frac cooling effect is long lasting as shown in FIG. 16. Realdata supports residual frac cooling. Production does not overcome theimpact of fracture cooling. The thermal radius of investigation, asshown in FIG. 17, indicates a wide variation of temperatures duringpumping and no temperature warmup down the length of the pipe.Temperature changes in magnitude may be misinterpreted if based solelyon possible DTS temperature changes. Thus, early time warmback (hours)data should not be used in analysis. As shown in FIG. 18, cross flow canbe observed through rapid thermal increases which are indicative ofgreater conduction. Thus in order to get past low radius ofinvestigation and crossflow effects, later time warmback after multipledays should be used for analysis.

In conclusion, temperature warmback data in an unconventional well is ameasure of the frac spatial density near-well, a concept which becomesapparent when sampling the SRV. DTS warmback data is a measure ofspatial efficiency and DAS during fracturing is a measure of volumedistribution efficiency, which are not the same thing.

For intervention data acquisitions: Leverage the concept of residualfrac cooling to get completion diagnostics without monitoring duringactual frac. More appropriate for completions test which alter fracgeometry and not fluid distribution. Combine with production loggingmethods if possible.

For permanent data acquisitions: Measure injection volume distributionswith DAS, not DTS. Temperature data viewed as the geometric/spatial toolto complement the DAS volumetric tool, not in competition with eachother.

CONCLUSIONS

The operational success of the Shale 1 pilot has demonstrated that theSRV in a shale can be drilled and sampled and that useful informationcan be gathered using the disclosed acquisition program. The mainoutcome of the disclosed acquisition program is an improvedunderstanding of the complex characteristics of the SRV. A compendium ofsignificant observations and conclusions include:

-   -   Permeability enhancement was realized through discrete fractures        rather than distributed matrix damage. The effective reservoir        permeability was presumed to be anisotropic. The fractures were        not evenly distributed spatially; thus, reservoir drainage may        be non-uniform.    -   The hydraulic fractures were numerous and broadly parallel.        There are many more fractures than perforation clusters.        Pre-existing natural fractures do not appear necessary to        achieve a complex, distributed fracture system.    -   Hydraulic fractures form swarms that, in some, but not all        stages, show a relationship to cluster spacing.    -   The hydraulic fracture trend was perpendicular to the minimum        horizontal in-situ stress. The fractures were steeply dipping        rather than vertical.    -   Fracture deflection, offset and branching at bedding surfaces        and other naturally occurring heterogeneities appeared to        significantly influence fracture complexity.    -   In the pilot area, the hydraulic fracture density decreased        above and laterally away from the producer. This indicates that        the hydraulically fractured volume could be on the order of two        to three times as broad, laterally, as it was tall. This shape        was generally consistent with the shape of the microseismic        event cloud.    -   The SRV was still grossly under sampled by the 7,700 ft. of        well-paths that cut through it in this project. This makes the        three-dimensional characteristics of the SRV difficult to        describe accurately.    -   Although the stimulation very efficiently fractured the        formation, proppant emplacement appeared to have been less        successful. While proppant was recovered in the cuttings, only        two cored hydraulic fractures contained in-situ evidence of        proppant. This left the location of most of the proppant        location undetermined. Sampling closer to the stimulated well        and below it may lead to better understanding of proppant        distribution.    -   The recorded pressure response at S1 and the coincident        inter-well DAS response qualified DAS as a fracture detection        tool. DAS indicates that some of the fractures propagate at        least 1,500 ft, which as supported by the extent of the        microseismic event cloud. DAS indicated fracture height growth        extends from the Buda to the Austin Chalk.    -   There was no direct statistical relationship between sampled        hydraulic fracture density and microseismic event density. There        is moderate correlation of microseismic events to fractures        dipping at greater than 70 degrees.    -   The stimulation monitoring by DTS/DAS showed that all        perforation clusters took fluid; however, the volume was not        equally distributed amongst clusters within a stage as planned.        In many stages, the plugs isolating the previous stage leaked        leading to over-flushing of some stages and possibly a less        efficient stimulation.

These observations affected Applicant's approach to completion design,well design and well spacing and stacking for the reservoir. Theobserved hydraulic fracture complexity and heterogeneity have caused are-examination of design standards and expectations regarding clusterspacing, effective proppant distribution, proppant propagation and thespatial extent of effective reservoir drainage. Furthermore, it hasemphasized significant challenges to forward modeling fracturepropagation, the spatial distribution of production performance and longterm multi-well interactions.

Though not used in the pilot study, the collected data can be combinedwith other techniques to accurately monitor and conduct well stimulationas well as modify the stimulation program as it proceeds.

The following references are incorporated by reference in their entiretyfor all purposes.

-   1. US-2014-0358444, “Method of Hydraulic Fracture Identification    Using Temperature” (2013-05-31)-   2. US-2018-0016890, “Hydraulic Fracture Analysis” (2013-05-31)-   3. US-2017-0260839, “DAS for Well Ranging” (2016-03-09)-   4. US-2017-0260842, “Low Frequency Distributed Acoustic Sensing”    (2016-03-09)-   5. US-2017-0260846, “Measuring Downhole Temperature by Combining    DAS/DTS Data” (2016-03-09)-   6. US-2017-0260849, “DAS Method of Estimating Fluid Distribution”    (2016-03-09)-   7. US-2017-0260854, “Hydraulic fracture monitoring by low-frequency    DAS” (2016-03-09)-   8. US-2017-0342814, “Low-Frequency DAS SNR Improvement” (2016-03-09)-   9. US-2018-0045040, “Production Logs from distributed acoustic    sensors,” (2016-03-09)-   10. Silverman, B. W. 1986 Density Estimation for Statistics and Data    Analysis. London: Chapman and Hall.-   11. Kulander, B. R., Dean, S. L., and Ward B. J. 1990. “Fractured    Core Analysis: Interpretation, Logging, and Use of Natural and    Induced Fractures in Core,” AAPG Methods in Exploration Series,    No 8. Tulsa, 1990-   12. Sheather, S J, Jones M C 1991 A reliable data-based bandwidth    selection method for kernel density. Journal of the Royal    Statistical Society. Series B (Methodological), pp 683-690-   13. Kevin T. Raterman, et al. “Sampling a Stimulated Rock Volume: An    Eagle Ford Example,” Unconventional Resources Technology Conference    (URTeC), 2017, URTeC: 2670034.-   14. Ge Jin & Baishali Roy “Hydraulic Fracture Geometry    Characterization Using Low-Frequency DAS Signal,” The Leading Edge    36(12):975-980—December 2017-   15. Kevin T. Raterman, et al., “Sampling a Stimulated Rock Volume:    An Eagle Ford Example,” SPE/AAPG/SEG Unconventional Resources    Technology Conference (URTeC), 24-26 July, Austin, Tex.,    USA 2670034. doi.org/10.15530/URTEC-2017-2670034

The invention claimed is:
 1. A method of optimizing the productionscheme of a hydrocarbon-containing reservoir comprising: a) collectingimage log, microseismic, Distributed Temperature Sensing (DTS),Distributed Acoustic Sensing (DAS), and pressure data from at least oneobservation well in a hydrocarbon-containing reservoir to form apre-stimulation data set; b) while fracturing at least one well in afirst fracture stimulation stage according to pre-determined fracturingparameters to form a set of fractures; c) collecting image log,microseismic, Distributed Temperature Sensing (DTS), DistributedAcoustic Sensing (DAS), and pressure data from at least said observationwell in the hydrocarbon-containing reservoir to form a strain responsedata set; d) identifying said set of fractures formed in step b) fromsaid strain response data set wherein said formation deformation ismechanically coupled with the strain rate during hydraulic fracturingand formation compression through stress shadowing; e) characterizingthe complexity of said set of fractures; f) updating said pre-determinedfracturing parameters based on said characterizing step; and, g)performing a second fracturing stimulation stage using said updatedfracturing parameters in step f).
 2. The method of claim 1, wherein saidimage log and/or microseismic data samples a stimulated reservoir volume(SRV).
 3. The method of claim 1, wherein said observation well is one ormore adjacent producer wells.
 4. The method of claim 1, wherein saidobservation well collects data from one or more adjacent producer wells.5. The method of claim 1, wherein said characterizing step furtherincludes modeling the stimulated reservoir volume (SRV) of saidreservoir.
 6. The method of claim 5, further comprising repeating stepsa) to f) of the method and updating the model of said SRV iteratively.7. The method of claim 1, wherein a relative extent of stimulatedreservoir volume (SRV) is estimated from microseismic and cross well DASfracture density spatially assigned through a statistical analysis ofdrill through data.
 8. A non-transitory computer readable storage mediumstoring a computer program product for characterizing a hydraulicfracturing stimulation, said computer program product comprising: a)integrated core data, image log data, microseismic data, DistributedTemperature Sensing (DTS) data, Distributed Acoustic Sensing (DAS) DATA,and pressure data from at least one observation well in ahydrocarbon-containing reservoir, b) integrated core data, image logdata, microseismic data, Distributed Temperature Sensing (DTS) data,Distributed Acoustic Sensing (DAS) DATA, and pressure data duringfracturing of a reservoir to form a strain response data set, c)computer code for estimating a stimulated reservoir volume (SRV) that isbeing fractured from the data in a) and b) wherein said computer codeidentifies said set of fractures formed in step b) from said strainresponse data set wherein formation deformation is mechanically coupledwith a strain rate during hydraulic fracturing and formation compressionthrough stress shadowing, d) computer code for characterizing thecomplexity of said set of fractures; e) computer code for modifyingfracturing parameters from step a) based on said characterizing step;and, f) computer code for performing a second fracturing stimulationusing said modified fracturing parameters in step e).
 9. Thenon-transitory computer readable storage medium storing a computerprogram product of claim 8, wherein modifying said fracturing parametersimproves hydrocarbon production.
 10. The non-transitory computerreadable storage medium storing a computer program product of claim 8,wherein said image log or microseismic data samples a stimulatedreservoir volume (SRV).
 11. The non-transitory computer readable storagemedium storing a computer program product of claim 8, wherein saidobservation well is one or more adjacent producer wells.
 12. Thenon-transitory computer readable storage medium storing a computerprogram product of claim 8, wherein said Distributed Temperature Sensing(DTS) data, Distributed Acoustic Sensing (DAS) DATA, and pressure dataare collected from one or more adjacent producer wells.
 13. Thenon-transitory computer readable storage medium storing a computerprogram product of claim 8, wherein said characterizing step furtherincludes modeling the stimulated reservoir volume (SRV) of saidreservoir.
 14. The non-transitory computer readable storage mediumstoring a computer program product of claim 13, further comprisingrepeating steps c) to e) and modifying the model of said SRViteratively.
 15. The non-transitory computer readable storage mediumstoring a computer program product of claim 8, wherein a relative extentof stimulated reservoir volume (SRV) is estimated from microseismic andcross well DAS fracture density spatially assigned through a statisticalanalysis of drill through data.